order
Ordering Permutation
order
returns a permutation which rearranges its first
argument into ascending or descending order, breaking ties by further
arguments. sort.list
is the same, using only one argument.
See the examples for how to use these functions to sort data frames,
etc.
Usage
order(..., na.last = TRUE, decreasing = FALSE)
sort.list(x, partial = NULL, na.last = TRUE, decreasing = FALSE, method = c("shell", "quick", "radix"))
Arguments
 ...
 a sequence of numeric, complex, character or logical vectors, all of the same length, or a classed R object.
 x
 an atomic vector.
 partial
 vector of indices for partial sorting.
(Non
NULL
values are not implemented.)  decreasing
 logical. Should the sort order be increasing or decreasing?
 na.last
 for controlling the treatment of
NA
s. IfTRUE
, missing values in the data are put last; ifFALSE
, they are put first; ifNA
, they are removed (see ‘Note’.)  method
 the method to be used: partial matches are allowed. The
default is
"shell"
except for some special cases: see ‘Details’.
Details
In the case of ties in the first vector, values in the second are used
to break the ties. If the values are still tied, values in the later
arguments are used to break the tie (see the first example).
The sort used is stable (except for method = "quick"
),
so any unresolved ties will be left in their original ordering.
Complex values are sorted first by the real part, then the imaginary part.
The sort order for character vectors will depend on the collating
sequence of the locale in use: see Comparison
.
The default method for sort.list
is a good compromise. Method
"quick"
is only supported for numeric x
with
na.last = NA
, and is not stable, but will be substantially
faster for long vectors. Method "radix"
is only implemented
for integer x
with a range of less than 100,000. For such
x
it is very fast (and stable), and hence is ideal for sorting
factorsas from R 3.0.0 it is the default method for factors with
less than 100,000 levels.
partial = NULL
is supported for compatibility with other
implementations of S, but no other values are accepted and ordering is
always complete.
For a classed R object, the sort order is taken from
xtfrm
: as its help page notes, this can be slow unless a
suitable method has been defined or is.numeric(x)
is
true. For factors, this sorts on the internal codes, which is
particularly appropriate for ordered factors.
Value

An integer vector unless any of the inputs has $2^31$ or
more elements, when it is a double vector.
Note
sort.list
can get called by mistake as a method for
sort
with a list argument, and gives a suitable error
message for list x
.
There is a historical difference in behaviour for na.last = NA
:
sort.list
removes the NA
s and then computes the order
amongst the remaining elements: order
computes the order
amongst the nonNA
elements of the original vector. Thus
x[order(x, na.last = NA)] zz < x[!is.na(x)]; zz[sort.list(x, na.last = NA)]both sort the non
NA
values of x
.
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
See Also
Examples
library(base)
require(stats)
(ii < order(x < c(1,1,3:1,1:4,3), y < c(9,9:1), z < c(2,1:9)))
## 6 5 2 1 7 4 10 8 3 9
rbind(x, y, z)[,ii] # shows the reordering (ties via 2nd & 3rd arg)
## Suppose we wanted descending order on y.
## A simple solution for numeric 'y' is
rbind(x, y, z)[, order(x, y, z)]
## More generally we can make use of xtfrm
cy < as.character(y)
rbind(x, y, z)[, order(x, xtfrm(cy), z)]
## Sorting data frames:
dd < transform(data.frame(x, y, z),
z = factor(z, labels = LETTERS[9:1]))
## Either as above {for factor 'z' : using internal coding}:
dd[ order(x, y, z), ]
## or along 1st column, ties along 2nd, ... *arbitrary* no.{columns}:
dd[ do.call(order, dd), ]
set.seed(1) # reproducible example:
d4 < data.frame(x = round( rnorm(100)), y = round(10*runif(100)),
z = round( 8*rnorm(100)), u = round(50*runif(100)))
(d4s < d4[ do.call(order, d4), ])
(i < which(diff(d4s[, 3]) == 0))
# in 2 places, needed 3 cols to break ties:
d4s[ rbind(i, i+1), ]
## rearrange matched vectors so that the first is in ascending order
x < c(5:1, 6:8, 12:9)
y < (x  5)^2
o < order(x)
rbind(x[o], y[o])
## tests of na.last
a < c(4, 3, 2, NA, 1)
b < c(4, NA, 2, 7, 1)
z < cbind(a, b)
(o < order(a, b)); z[o, ]
(o < order(a, b, na.last = FALSE)); z[o, ]
(o < order(a, b, na.last = NA)); z[o, ]
## Not run:
# ## speed examples for long vectors:
# x < factor(sample(letters, 1e6, replace = TRUE))
# system.time(o < sort.list(x)) ## 0.4 secs
# stopifnot(!is.unsorted(x[o]))
# system.time(o < sort.list(x, method = "quick", na.last = NA)) # 0.1 sec
# stopifnot(!is.unsorted(x[o]))
# system.time(o < sort.list(x, method = "radix")) # 0.01 sec
# stopifnot(!is.unsorted(x[o]))
# xx < sample(1:26, 1e7, replace = TRUE)
# system.time(o < sort.list(xx, method = "radix")) # 0.1 sec
# xx < sample(1:100000, 1e7, replace = TRUE)
# system.time(o < sort.list(xx, method = "radix")) # 0.5 sec
# system.time(o < sort.list(xx, method = "quick", na.last = NA)) # 1.3 sec
# ## End(Not run)
Community examples
[Exercise files for LinkedIn Learning](https://linkedinlearning.pxf.io/rweekly_sortmerge) ```r # Need a dataframe to play with data("ChickWeight") # a builtin dataset # data.frames: order  ChickWeight$weight # produces unsorted list of "weight" values sort(ChickWeight$weight) # sorts a vector order(ChickWeight$weight) # What is this? answer: sorted row numbers  not values ChickWeight[196,] #lists the observation with the smallest weight ChickWeight[order(ChickWeight$weight),] # comma because [ row  comma  column ] ```
``` ##Note that order() is different from rank(). rank() will return you rank of the elements while order() returns the ranked element's position in the original list: > a < c(45,50,10,96) > order(a) [1] 3 1 2 4 > rank(a) [1] 2 3 1 4 ```